Fingerprint verification method and apparatus

ABSTRACT

A fingerprint verification method and a fingerprint verification apparatus performing the fingerprint verification method are disclosed. The fingerprint verification apparatus determines a first similarity between a query fingerprint image and each of registered fingerprint images, selects a target registered fingerprint image group from registered fingerprint image groups based on the first similarity, determines a second similarity between the query fingerprint image and each of registered fingerprint images in the target registered fingerprint image group based on matching relationship information between the registered fingerprint images in the target registered fingerprint image group, and determines whether fingerprint verification of the query fingerprint image is successful based on the second similarity.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit under 35 USC § 119(a) of KoreanPatent Application No. 10-2017-0078021 filed on Jun. 20, 2017, in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to fingerprint verification.

2. Description of Related Art

Biometric authentication is used to authenticate a user by using theusers' biological features, such as, fingerprints, irises, voices,facial features, blood vessels, or other biological characteristics.Such biological characteristics used in user authentication vary fromperson to person and rarely change during the lifetime of a user.Further, the biological characteristics pose a low risk of theft orimitation, providing high security authentication. Unlike fobs and otherexternal objects, individuals do not need to exert any efforts to carryaround such characteristics at all times, and thus, may not sufferinconvenience in using the biological characteristics. Fingerprintverification approaches are most commonly used due to their high levelof convenience, security, and economic efficiency. A fingerprintverification approach may include comparing a fingerprint image of auser requesting user authentication to a previously registeredfingerprint image and determining whether to authenticate the user basedon a result of the comparing.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is this Summaryintended to be used as an aid in determining the scope of the claimedsubject matter.

In one general aspect, there is provided a fingerprint verificationmethod including receiving a query fingerprint image, determining afirst similarity between the query fingerprint image and each ofregistered fingerprint images, selecting a target registered fingerprintimage group from registered fingerprint image groups based on the firstsimilarity, determining a second similarity between the queryfingerprint image and each of registered fingerprint images in thetarget registered fingerprint image group based on matching relationshipinformation between the registered fingerprint images in the targetregistered fingerprint image group, and determining whether fingerprintverification of the query fingerprint image is successful based on thesecond similarity.

The matching relationship information may include information associatedwith a matching rotation angle between the registered fingerprint imagesin the target registered fingerprint image group, and the matchingrotation angle is determined in a fingerprint registration process.

The selecting of the target registered fingerprint image group mayinclude selecting a target registered fingerprint image, which is aregistered fingerprint image having a greatest first similarity amongthe registered fingerprint images, and selecting, to be the targetregistered fingerprint image group, a registered fingerprint image groupincluding the target registered fingerprint image.

The determining of the second similarity may include determining asecond similarity between the query fingerprint image and the targetregistered fingerprint image, and determining a second similaritybetween the query fingerprint image and another registered fingerprintimage included in the target registered fingerprint image group based ona matching rotation angle between the query fingerprint image and thetarget registered fingerprint image, and the matching relationshipinformation.

The matching relationship information may include a matching rotationangle between the target registered fingerprint image and a referenceregistered fingerprint image included in the target registeredfingerprint image group, and a matching rotation angle between thereference registered fingerprint image and the other registeredfingerprint image.

The determining of the second similarity may include determining amatching rotation angle between the query fingerprint image and thetarget registered fingerprint image, arranging the query fingerprintimage and the target registered fingerprint image based on thedetermined matching rotation angle, and determining a second similaritybetween the query fingerprint image and the target registeredfingerprint image in a matching region in which the arranged queryfingerprint image and the arranged target registered fingerprint imagematch each other.

The second similarity may be determined with a greater computationalcomplexity than used to determine the first similarity.

The determining of whether the fingerprint verification is successfulmay include determining whether the fingerprint verification issuccessful based on a second similarity of a matching region between thequery fingerprint image and at least one registered fingerprint imageincluded in the target registered fingerprint image group.

In one general aspect, there is provided a fingerprint registrationmethod including receiving a current fingerprint image for fingerprintregistration, determining a similarity between the current fingerprintimage and a previously registered fingerprint image, classifying thecurrent fingerprint image and the registered fingerprint image into aregistered fingerprint image group, in response to the similaritysatisfying a condition, determining matching relationship informationbetween the current fingerprint image and a reference registeredfingerprint image in the registered fingerprint image group, and storingthe determined matching relationship information.

The determining of the matching relationship information may includedetermining a matching rotation angle between the current fingerprintimage and the reference registered fingerprint image.

The classifying may include classifying the current fingerprint imageand the registered fingerprint image into the registered fingerprintimage group, in response to the similarity being greater than athreshold value.

The fingerprint registration method may include generating a newregistered fingerprint image group including the current fingerprintimage, in response to the similarity not satisfying the condition, anddetermining the current fingerprint image to be a reference registeredfingerprint image in the generated new registered fingerprint imagegroup.

The matching relationship information may include a matching rotationangle between the current fingerprint image and the reference registeredfingerprint image, and a group identifier of the registered fingerprintimage group assigned to the current fingerprint image.

In one general aspect, there is provided a fingerprint verificationapparatus including a processor configured to receive a queryfingerprint image, determine a first similarity between the queryfingerprint image and each of registered fingerprint images, select atarget registered fingerprint image group from registered fingerprintimage groups based on the first similarity, determine a secondsimilarity between the query fingerprint image and each of registeredfingerprint images in the target registered fingerprint image groupbased on matching relationship information between the registeredfingerprint images in the target registered fingerprint image group, anddetermine whether fingerprint verification of the query fingerprintimage is successful based on the second similarity.

The processor may be configured to select a target registeredfingerprint image, which is a registered fingerprint image having agreatest first similarity among the registered fingerprint images, andto select a registered fingerprint image group including the targetregistered fingerprint image to be the target registered fingerprintimage group.

The processor may be configured to determine a second similarity betweenthe query fingerprint image and the target registered fingerprint image,and to determine a second similarity between the query fingerprint imageand another registered fingerprint image included in the targetregistered fingerprint image group based on a matching rotation anglebetween the query fingerprint image and the target registeredfingerprint image and the matching relationship information.

The matching relationship information may include information associatedwith a matching rotation angle between the registered fingerprint imagesin the target registered fingerprint image group, and the matchingrotation angle is determined in a fingerprint registration process.

In one general aspect, there is provided a computing apparatus includinga fingerprint sensor configured to obtain a current fingerprint imagefor fingerprint registration, and a processor configured to determine asimilarity between the current fingerprint image and a previouslyregistered fingerprint image, classify the current fingerprint image andthe registered fingerprint image into a same registered fingerprintimage group, in response to the determined similarity satisfying acondition, determine matching relationship information between thecurrent fingerprint image and a reference registered fingerprint imagein the registered fingerprint image group, and store the determinedmatching relationship information.

The matching relationship information may include a matching rotationangle between the current fingerprint image and the reference registeredfingerprint image and a group identifier of the registered fingerprintimage group assigned to the current fingerprint image.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a fingerprintverification method.

FIGS. 2A and 2B are diagrams illustrating examples of matching between aquery fingerprint image and a registered fingerprint image.

FIG. 3 is a diagram illustrating an example of a fingerprintregistration method.

FIG. 4 is a diagram illustrating an example of a method of classifyingregistered fingerprint images into registered fingerprint image groups.

FIGS. 5A through 5D are diagrams illustrating an example of a method ofclassifying registered fingerprint images into registered fingerprintimage groups.

FIG. 6 is a diagram illustrating an example of a method of determiningmatching relationship information associated with a matchingrelationship between registered fingerprint images.

FIG. 7 is a diagram illustrating an example of a fingerprintverification method.

FIG. 8 is a diagram illustrating another example of a fingerprintverification method.

FIG. 9 is a diagram illustrating an example of a fingerprintverification method.

FIGS. 10 through 12 are diagrams illustrating examples of a method ofdetermining a similarity between fingerprint images based on aFourier-Mellin transform.

FIG. 13 is a diagram illustrating an example of a method of determiningwhether fingerprint verification is successful based on a matchingregion-based similarity.

FIG. 14 is a diagram illustrating an example of a fingerprintverification apparatus.

FIG. 15 is a diagram illustrating an example of a computing apparatus.

Throughout the drawings and the detailed description, unless otherwisedescribed or provided, the same drawing reference numerals will beunderstood to refer to the same or like elements, features, andstructures. The drawings may not be to scale, and the relative size,proportions, and depiction of elements in the drawings may beexaggerated for clarity, illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known after an understanding of thedisclosure of this application may be omitted for increased clarity andconciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or systems described herein that will beapparent after an understanding of the disclosure of this application.

Terms such as first, second, A, B, (a), (b), and the like may be usedherein to describe components. Each of these terminologies is not usedto define an essence, order, or sequence of a corresponding componentbut used merely to distinguish the corresponding component from othercomponent(s). For example, a first component may be referred to as asecond component, and similarly the second component may also bereferred to as the first component.

It should be noted that if it is described in the specification that onecomponent is “connected,” “coupled,” or “joined” to another component, athird component may be “connected,” “coupled,” and “joined” between thefirst and second components, although the first component may bedirectly connected, coupled or joined to the second component. Inaddition, it should be noted that if it is described in thespecification that one component is “directly connected” or “directlyjoined” to another component, a third component may not be presenttherebetween. Likewise, expressions, for example, “between” and“immediately between” and “adjacent to” and “immediately adjacent to”may also be construed as described in the foregoing.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. For example, asused herein, the singular forms “a,” “an,” and “the,” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. In addition, the use of the term ‘may’ herein with respect toan example or embodiment, e.g., as to what an example or embodiment mayinclude or implement, means that at least one example or embodimentexists where such a feature is included or implemented while allexamples and embodiments are not limited thereto.

FIG. 1 is a diagram illustrating an example of a fingerprintverification method.

Fingerprint verification refers to a verification method used todetermine whether a user is a valid user or not, and verify a valid userin applications, such as, for example, user log-in, payment services,financial services, and access control. Referring to FIG. 1, afingerprint verification apparatus that performs such a verificationmethod is included in, or represented by, a computing apparatus 100.

In an example, the computing apparatus 100 includes various types ofproducts such as, for example, an intelligent agent, a mobile phone, acellular phone, a smart phone, a wearable smart device (such as, a ring,a watch, a pair of glasses, glasses-type device, a bracelet, an anklebracket, a belt, a necklace, an earring, a headband, a helmet, a deviceembedded in the cloths, or an eye glass display (EGD)), a server, apersonal computer (PC), a laptop, a notebook, a subnotebook, a netbook,an ultra-mobile PC (UMPC), a tablet personal computer (tablet), aphablet, a mobile internet device (MID), a personal digital assistant(PDA), an enterprise digital assistant (EDA), a digital camera, adigital video camera, a portable game console, an MP3 player, aportable/personal multimedia player (PMP), a handheld e-book, an ultramobile personal computer (UMPC), a portable lab-top PC, a globalpositioning system (GPS) navigation, a personal navigation device,portable navigation device (PND), a handheld game console, an e-book, ahigh definition television (HDTV), a smart appliance, a home appliance,a biometrics-based door lock, a security device, a financial servicedevice, a kiosk, communication systems, image processing systems,graphics processing systems, various Internet of Things (IoT) devicesthat are controlled through a network, a smart vehicle, other consumerelectronics/information technology (CE/IT) device, or any other devicecapable of wireless communication or network communication consistentwith that disclosed herein.

The examples may be applied to image processing for user authenticationusing a smart phone, a mobile device, a smart home system, anintelligent vehicle, and an automated teller machine (ATM). Further, theexamples may also be applied to an intelligent vehicle system thatautomatically starts by authenticating a user. The examples may also beapplied to image processing for user authentication in an intelligentvehicle, an apparatus for automatic driving, a smart home environment, asmart building environment, a smart office environment, officeautomation, and a smart electronic secretary system.

In the example of the computing apparatus 100 illustrated in FIG. 1, thecomputing apparatus 100 is a smartphone. In an example, the computingapparatus 100 obtains a fingerprint image 120 through a fingerprintsensor 110, and determine whether a user attempting access the computingapparatus 100 is a valid user or not based on the obtained fingerprintimage 120. For example, when the user inputs a fingerprint of the userto the fingerprint sensor 110 to cancel a lock state of the computingapparatus 100, the computing apparatus 100 compares the fingerprintimage 120 obtained through the fingerprint sensor 110 to at least one ofregistered fingerprint images 142, 144, 146 stored in a database DB 130,and determines whether to cancel the lock state of the computingapparatus 100 based on a result of the comparing. A fingerprint imageinput for the fingerprint verification will be hereinafter referred toas a query fingerprint image.

In an example, a valid user registers fingerprint information of thevalid user in the computing apparatus 100, in advance, in a fingerprintregistration process. In the fingerprint registration process, the validuser may input, to the fingerprint sensor 110, a fingerprint the validuser desires to register in the computing apparatus 100, and thefingerprint sensor 110 may then obtain at least one fingerprint image.In an example, the obtained fingerprint image is registered as aregistered fingerprint image. In an example, the registered fingerprintimage is stored in the DB 130 or the computing apparatus 100 or at anexternal storage medium, such as, for example, cloud storage. The storedregistered fingerprint image is used in a fingerprint verificationprocess to match a query fingerprint image and determine whether afingerprint pattern in the query fingerprint image corresponds to thevalid user.

FIGS. 2A and 2B are diagrams illustrating examples of matching between aquery fingerprint image and a registered fingerprint image.

Referring to FIG. 2A, in a fingerprint verification process, success orfailure of fingerprint verification is determined based on a comparisonof a query fingerprint image 210 and at least one registered fingerprintimage, for example, a registered fingerprint image 220. To comparefingerprint patterns of the two fingerprint images, matching between thequery fingerprint image 210 and the registered fingerprint image 220 isperformed. In an example, the matching used herein refers to anoperation to determine or discover a matching region 230 in which thequery fingerprint image 210 and the registered fingerprint image 220share a same or similar fingerprint pattern. In an example, the matchingincludes rotating and/or translating at least one of the queryfingerprint image 210 or the registered fingerprint image 220.

A size of the query fingerprint image 210 and a size of the registeredfingerprint image 220 are determined by a sensing region of afingerprint sensor that is configured to sense a fingerprint image. Asthe fingerprint sensor has become smaller in size, a size of the sensingregion of the fingerprint sensor has also decreased gradually. The sizeof the sensing region may restrict of the amount of information obtainedby the fingerprint sensor and included in the query fingerprint image210 and/or the registered fingerprint image 220 to only a portion of anentire fingerprint, or a partial fingerprint. Thus, as illustrated inFIG. 2B, a small-size matching region 260 between a query fingerprintimage 240 and a registered fingerprint image 250 may be obtained.However, using such a small-size matching region 260 may result inincorrect matching between the query fingerprint image 240 and theregistered fingerprint image 250. Thus, a small-size of the matchingregion 260 means that there may be insufficient information or clues foraccurately matching between fingerprint images.

In a fingerprint registration process, a user may register a fingerprintmultiple times for a same finger. The user may tend to attempt toregister fingerprints in a similar pose, and thus, there is likelihoodthat a size of a matching region between registered fingerprint imagesof the finger is large. A method of precalculating matching relationshipinformation associated between registered fingerprint images,pre-storing the calculated matching relationship information in thefingerprint registration process, and then matching a query fingerprintimage and a registered fingerprint image based on corresponding matchingrelationship information in a fingerprint verification process will bedescribed hereinafter. Thus, matching between the query fingerprintimage and the registered fingerprint image may be performed moreaccurately, and fingerprint verification may be performed more rapidly.The method will be described hereinafter in greater detail withreference to the accompanying drawings.

FIG. 3 is a diagram illustrating an example of a fingerprintregistration method. The operations in FIG. 3 may be performed in thesequence and manner as shown, although the order of some operations maybe changed or some of the operations omitted without departing from thespirit and scope of the illustrative examples described. Many of theoperations shown in FIG. 3 may be performed in parallel or concurrently.One or more blocks of FIG. 3, and combinations of the blocks, can beimplemented by special purpose hardware-based computer that perform thespecified functions, or combinations of special purpose hardware andcomputer instructions. In addition to the description of FIG. 3 below,the descriptions of FIGS. 1-2 are also applicable to FIG. 3, and areincorporated herein by reference. Thus, the above description may not berepeated here.

In a fingerprint registration process, a user may register a fingerprintfor at least one finger of the user. For example, the user registers afingerprint of an index finger multiple times, and a fingerprint imageis captured each time the user registers a fingerprint and the capturedfingerprint image is stored as a registered fingerprint image. In anexample, registered fingerprint images may be stored without anadditional operation, such as stitching, to form an entire fingerprintthrough image synthesis.

In operation 310, a computing apparatus receives a current fingerprintimage for fingerprint registration. In an example, the currentfingerprint image refers to a fingerprint image that is obtained througha fingerprint sensor. Although not illustrated, in an example, thecomputing apparatus preprocesses the received current fingerprint image.In an example, the image preprocessing is performed to, for example,improve a quality of the current fingerprint image and to adjust a sizeof the current fingerprint image so that the current fingerprint imageis in a more suitable form for fingerprint registration. The imagepreprocessing includes, for example, removing noise included in thecurrent fingerprint image, increasing a contrast of the currentfingerprint image, deblurring the current fingerprint image, performingwarping to correct a distortion in the current fingerprint image,binarizing the current fingerprint image, normalizing a size of thecurrent fingerprint image, and cropping the current fingerprint image.

When the current fingerprint image is a first fingerprint image to beregistered, i.e. when there is no other previously registeredfingerprint image, the current fingerprint image may be registeredwithout following operations 320 through 370. The computing apparatusmay generate a new registered fingerprint image group including thecurrent fingerprint image, and set the current fingerprint image to be areference registered fingerprint image in the generated new registeredfingerprint image group. In an example, the computing apparatus performsthe fingerprint registration on a fingerprint image that is receivedsubsequently in accordance with operations 320 through 370.

In operation 320, the computing apparatus determines a similaritybetween the current fingerprint image and a previously registeredfingerprint image. In an example, the computing apparatus calculates thesimilarity between fingerprint patterns of the current fingerprint imageand the registered fingerprint image in a matching region. In anexample, the matching region refers to a region in which a fingerprintpattern of the current fingerprint image and a fingerprint pattern ofthe registered fingerprint image are same or similar to each other. Ahigh similarity or a low similarity used herein indicates the level ofrelative similarities of fingerprint patterns of two fingerprint images.In an example, when there are plural registered fingerprint images, thecomputing apparatus may determine a similarity between the currentfingerprint image and each of the registered fingerprint images.

In one example, the computing apparatus uses, as the similarity, afeature value obtained by performing a fast Fourier transform (FFT) onthe current fingerprint image and the registered fingerprint image. Inan example, the computing apparatus calculates the similarity through animage frequency information-based matching method such as aFourier-Mellin transform. The similarity may be determined based onphase correlation information obtained using the Fourier-Mellintransform. In another example, the similarity may be determined based onrotation information and translation information associated with arotation and a translation observed between the current fingerprintimage and the registered fingerprint image in addition to the phasecorrelation information. Determination of similarity based on theFourier-Mellin transform will be further described with reference toFIGS. 10 through 12. However, the determining of the similarity betweenthe current fingerprint image and the registered fingerprint image isnot limited to using the Fourier-Mellin transform, and various othermethods may be used to determine a similarity between fingerprintpatterns. For example, a similarity may be determined based on adistribution or form of feature points extracted from the fingerprintpatterns.

In operation 330, the computing apparatus determines whether thedetermined similarity satisfies a condition. In one example, thecomputing apparatus determines whether the similarity is greater than athreshold value. The computing apparatus determines that the similaritysatisfies the condition in response to the similarity being greater thanthe threshold value, and that the similarity does not satisfy thecondition in response to the similarity not being greater than thethreshold value.

In operation 350, when the similarity satisfies the condition, thecomputing apparatus classifies the current fingerprint image and theregistered fingerprint image into a same registered fingerprint imagegroup. Registered fingerprint images may be classified into registeredfingerprint image groups based on their similarities, and thus, morethan one registered fingerprint image groups may be generated as aresult of the classifying. For example, when a similarity between thecurrent fingerprint image and only one of the registered fingerprintimages satisfies the condition, the current fingerprint image may bedetermined to be included in a registered fingerprint image group towhich the one registered fingerprint image belongs. In another example,when similarities between the current fingerprint image and pluralregistered fingerprint images satisfy the condition, and the pluralregistered fingerprint images belong to different registered fingerprintimage groups, the different registered fingerprint image groups may bemerged into a single registered fingerprint image group and the currentfingerprint image may be determined to be included in the singleregistered fingerprint image group. In an example, a group identifier ofthe registered fingerprint image group including the current fingerprintimage may be assigned to the current fingerprint image.

In an example, each of the registered fingerprint image groups has areference registered fingerprint image. The reference registeredfingerprint image refers to a registered fingerprint image that is usedas a reference for a registered fingerprint image group, which includesthe reference registered fingerprint image. For example, a registeredfingerprint image that is included first in each of the registeredfingerprint image groups may be set to be the reference registeredfingerprint image.

In operation 360, the computing apparatus determines matchingrelationship information associated with a matching relationship betweenthe current fingerprint image and a reference registered fingerprintimage included in the registered fingerprint image group obtained inoperation 350. The matching relationship information includes, forexample, information associated with a matching rotation angle betweenthe current fingerprint image and the reference registered fingerprintimage and the group identifier of the registered fingerprint image groupassigned to the current fingerprint image. The matching rotation anglerefers to an angle by which a fingerprint image needs to be rotated formatching. For example, the matching rotation angle may be an angle bywhich the current fingerprint image needs to be rotated relative to thereference registered fingerprint image for matching.

In operation 370, the computing apparatus stores the matchingrelationship information determined in operation 360. The computingapparatus updates previously stored matching relationship information ofthe registered fingerprint image group including the current fingerprintimage based on the matching relationship information of the currentfingerprint image. In an example, the updating is performed by addingthe matching relationship information of the current fingerprint imageto the previously stored matching relationship information.

In operation 340, when it is determined that the similarity does notsatisfy the condition, the computing apparatus generates a newregistered fingerprint image group including the current fingerprintimage, and assigns a group identifier of the new registered fingerprintimage group to the current fingerprint image. The computing apparatusdetermines the current fingerprint image to be a reference registeredfingerprint image of the new registered fingerprint image group. Amatching rotation angle of the current fingerprint image may be set tobe a reference value, for example, 0 degrees (°), and matchingrelationship information including information associated with thematching rotation angle of the current fingerprint image and the groupidentifier assigned to the current fingerprint image may be stored.

As described above, in a fingerprint registration process, registeredfingerprint images may be classified into registered fingerprint imagegroups based on similarities, and the registered fingerprint images maybe stored along with matching relationship information associated with amatching relationship between registered fingerprint images classifiedinto a same registered fingerprint image group. For example, matchingrelationship information associated with a matching relationship betweena reference registered fingerprint image included in a registeredfingerprint image group and another registered fingerprint imageincluded in the registered fingerprint image group may be stored. Thematching relationship information may include a group identifierassigned to the registered fingerprint images included in the registeredfingerprint image group, and relative matching rotation angles betweenthe registered fingerprint images included in the registered fingerprintimage group. In an example, the matching relationship information may berepresented in Table 1.

TABLE 1 Identifier of Group identifier of Relative matching rotationregistered registered fingerprint angle in registered fingerprintfingerprint image image group image group (unit: degree [°]) 1 A 0.0 2 B0.0 3 A −10.0 . . . . . . . . . N B 15.0

For example, as illustrated in Table 1, the matching relationshipinformation may include information associated with identifiers assignedto identify registered fingerprint images, for example, 1, 2, 3, . . . ,N, group identifiers assigned to identify registered fingerprint imagegroups to which corresponding registered fingerprint images belong, forexample, A and B, and relative matching rotation angles, for example,0.0, −10.0, . . . , 15.0, between registered fingerprint images includedin a same registered fingerprint image group. The matching relationshipinformation may be used to match a query fingerprint image and aregistered fingerprint image in a fingerprint verification process, andto determine a matching rotation angle between the query fingerprintimage and the registered fingerprint image more rapidly and accurately.In a case of a small-size matching region between the query fingerprintimage and the registered fingerprint image, there may be a highprobability that the matching rotation angle is incorrectly determinedwhen performing the matching. However, using the matching relationshipinformation, a more accurate matching rotation angle is determined, andthe matching relationship information reduces a computational complexityand an amount of resources that are needed for matching fingerprintimages.

FIG. 4 is a diagram illustrating an example of a method of classifyingregistered fingerprint images into registered fingerprint image groups.The operations in FIG. 4 may be performed in the sequence and manner asshown, although the order of some operations may be changed or some ofthe operations omitted without departing from the spirit and scope ofthe illustrative examples described. Many of the operations shown inFIG. 4 may be performed in parallel or concurrently. One or more blocksof FIG. 4, and combinations of the blocks, can be implemented by specialpurpose hardware-based computer that perform the specified functions, orcombinations of special purpose hardware and computer instructions. Inaddition to the description of FIG. 4 below, the descriptions of FIGS.1-3 are also applicable to FIG. 4, and are incorporated herein byreference. Thus, the above description may not be repeated here forbrevity.

Referring to FIG. 4, in operation 410, a computing apparatus receives acurrent fingerprint image that is input for fingerprint registration. Inoperation 420, the computing apparatus determines the presence of aregistered fingerprint image that has not been compared to the currentfingerprint image. When the registered fingerprint image is not present,a fingerprint registration process for the current fingerprint image isterminated.

In operation 430, when it is determined that the registered fingerprintimage that has not been compared to the current fingerprint image ispresent, the computing apparatus calculates a similarity between thecurrent fingerprint image and the registered fingerprint image. Inoperation 440, the computing apparatus determines whether the similarityis greater than a threshold value. In operation 450, when the similarityis not greater than the threshold value, the computing apparatusgenerates a new registered fingerprint image group including the currentfingerprint image.

In operation 460, when the similarity is greater than the thresholdvalue, the computing apparatus determines whether a group identifier ofa registered fingerprint image group assigned to the current fingerprintimage is present. When the current fingerprint image is to be initiallycompared to the registered fingerprint image, the group identifier isyet to be assigned to the current fingerprint image. When the currentfingerprint image is previously compared to another registeredfingerprint image, the group identifier assigned to the currentfingerprint image is present.

In operation 470, when it is determined that the group identifierassigned to the current fingerprint image is not present, the computingapparatus adds the current fingerprint image to a registered fingerprintimage group including the registered fingerprint image. The groupidentifier of the registered fingerprint image group may be assigned tothe current fingerprint image.

In operation 480, when it is determined that the group identifierassigned to the current fingerprint image is present, the computingapparatus merges the registered fingerprint image group including thecurrent fingerprint image and the registered fingerprint image groupincluding the registered fingerprint image. Thus, the registeredfingerprint image group including the current fingerprint image and theregistered fingerprint image group including the registered fingerprintimage may be set to be a same registered fingerprint image group, and asame group identifier may be assigned to the current fingerprint imageand the registered fingerprint image. However, when the group identifierof the current fingerprint image and the group identifier of theregistered fingerprint image are the same, i.e., when the registeredfingerprint image group including the current fingerprint image and theregistered fingerprint image group including the registered fingerprintimage are the same, such merging may not be needed.

After any one of operations 450, 470, and 480 is completed, theoperations described in the foregoing may be performed again on thecurrent fingerprint image and another registered fingerprint image.

FIGS. 5A through 5D are diagrams illustrating an example of a method ofclassifying registered fingerprint images into registered fingerprintimage groups. For convenience of description, fingerprint patterns offingerprint images are omitted in FIGS. 5A through 5D, 6, and 9.

FIG. 5A illustrates an example of a method of classifying a fingerprintimage 520 into a registered fingerprint group. In this example,registered fingerprint images 510 and 515 are already included in aregistered fingerprint image group A, and the fingerprint image 520 isnewly input for fingerprint registration. When a similarity between thefingerprint image 520 and the registered fingerprint image 515 isgreater than a threshold value, the fingerprint image 520 is classifiedinto the registered fingerprint image group A and assigned with a groupidentifier of the registered fingerprint image group A.

FIG. 5B illustrates an example of a method of classifying a newfingerprint image 530 into a registered fingerprint image group afterthe classifying of the fingerprint image 520 is completed. Referring toFIG. 5B, the registered fingerprint images 510, 515, and 520 areclassified into a same registered fingerprint image group A 525. When asimilarity between the fingerprint image 530 and each of the registeredfingerprint images 510, 515, and 520 is not greater than the thresholdvalue, a new registered fingerprint image group B is generated for thefingerprint image 530 and a group identifier of the registeredfingerprint image group B is assigned to the fingerprint image 530.

FIG. 5C illustrates an example of a method of classifying a newfingerprint image 535 into a registered fingerprint image group afterthe classifying of the fingerprint image 530 is completed. Referring toFIG. 5C, the registered fingerprint image group A 525 and a registeredfingerprint image group B 540 are present. When a similarity between thefingerprint image 535 and the registered fingerprint image 530 isgreater than the threshold value, the fingerprint image 535 isclassified into the registered fingerprint image group B 540 andassigned with a group identifier of the registered fingerprint imagegroup B 540.

FIG. 5D illustrates an example of a method of classifying a newfingerprint image 545 into a registered fingerprint image group afterthe classifying of the fingerprint image 535 is completed. In thisexample, when a similarity between the fingerprint image 545 and each ofthe registered fingerprint images 515 and 535 is greater than thethreshold value, corresponding registered fingerprint image groups aremerged. Thus, registered fingerprint image groups A and B are mergedinto a single registered fingerprint image group 550.

FIG. 6 is a diagram illustrating an example of a method of determiningmatching relationship information associated with a matchingrelationship between registered fingerprint images.

In this example, a registered fingerprint image 605 is included in aregistered fingerprint image group A. In stage S1, a similarity and amatching rotation angle between a fingerprint image 610 that is inputfor fingerprint registration and the registered fingerprint image 605included in the registered fingerprint image group A are determined asillustrated in Table 2.

TABLE 2 Matching rotation angle Similarity (unit: degree [°]) Registeredfingerprint image 605 0.80 42.1 ⇔ fingerprint image 610

In this example, when a threshold value is 0.95, the similarity betweenthe fingerprint image 610 and the registered fingerprint image 605 isnot greater than the threshold value, and thus, a new registeredfingerprint image group B including the fingerprint image 610 isgenerated. Here, corresponding matching relationship information isillustrated in Table 3. The registered fingerprint image 605 and thefingerprint image 610 are set to be a reference registered fingerprintimage of the registered fingerprint image group A and a referenceregistered fingerprint image of the registered fingerprint image groupB, respectively.

TABLE 3 Group identifier of Relative matching rotation registered anglein registered Identifier of registered fingerprint fingerprint imagegroup fingerprint image image group (unit: degree [°]) Registeredfingerprint A 0.0 image 605: 1 Registered fingerprint B 0.0 image 610: 2

In stage S2, a fingerprint image 615 is input for fingerprintregistration, and a similarity and a matching rotation angle between thefingerprint image 615 and each of the registered fingerprint images 605and 610 are determined as illustrated in Table 4.

TABLE 4 Matching rotation angle Similarity (unit: degree [°]) Registeredfingerprint image 605 0.97 5.3 ⇔ fingerprint image 615 Registeredfingerprint image 610 0.71 304.2 ⇔ fingerprint image 615

A similarity between the fingerprint image 615 and the registeredfingerprint image 605 is greater than the threshold value, for example,0.95, and a similarity between the fingerprint image 615 and theregistered fingerprint image 610 is not greater than the thresholdvalue, and thus the fingerprint image 615 is classified into theregistered fingerprint image group A to which the registered fingerprintimage 605 belongs. Here, corresponding matching relationship informationis illustrated in Table 5.

TABLE 5 Group identifier of Relative matching rotation registered anglein registered Identifier of registered fingerprint fingerprint imagegroup fingerprint image image group (unit: degree [°]) Registeredfingerprint A 0.0 image 605: 1 Registered fingerprint B 0.0 image 610: 2Registered fingerprint A 5.3 image 615: 3

In stage S3, a fingerprint image 620 is input for fingerprintregistration, and a similarity and a matching rotation angle between thefingerprint image 620 and each of the registered fingerprint images 605,610, and 615 are determined as illustrated in Table 6.

TABLE 6 Matching rotation angle Similarity (unit: degree [°]) Registeredfingerprint image 605 0.96 10.6 ⇔ fingerprint image 620 Registeredfingerprint image 610 0.74 191.3 ⇔ fingerprint image 620 Registeredfingerprint image 615 0.81 180.0 ⇔ fingerprint image 620

A similarity between the fingerprint image 620 and the registeredfingerprint image 605 is greater than the threshold value, for example,0.95, and a similarity between the fingerprint image 620 and each of theregistered fingerprint images 610 and 615 is not greater than thethreshold value, and thus the fingerprint image 620 is classified intothe registered fingerprint image group A to which the registeredfingerprint image 605 belongs. Here, corresponding matching relationshipinformation is illustrated in Table 7.

TABLE 7 Group identifier of Relative matching rotation registered anglein registered Identifier of registered fingerprint fingerprint imagegroup fingerprint image image group (unit: degree [°]) Registeredfingerprint A 0.0 image 605: 1 Registered fingerprint B 0.0 image 610: 2Registered fingerprint A 5.3 image 615: 3 Registered fingerprint A 10.6image 620: 4

According to another example, after classifying is performed on allfingerprint images to be registered, a relative matching rotation anglebetween registered fingerprint images included in a same registeredfingerprint image group may be determined for each registeredfingerprint image group.

FIG. 7 is a flowchart illustrating an example of a fingerprintverification method. The operations in FIG. 7 may be performed in thesequence and manner as shown, although the order of some operations maybe changed or some of the operations omitted without departing from thespirit and scope of the illustrative examples described. Many of theoperations shown in FIG. 7 may be performed in parallel or concurrently.One or more blocks of FIG. 7, and combinations of the blocks, can beimplemented by special purpose hardware-based computer that perform thespecified functions, or combinations of special purpose hardware andcomputer instructions. In addition to the description of FIG. 7 below,the descriptions of FIGS. 1-6 are also applicable to FIG. 7, and areincorporated herein by reference. Thus, the above description may not berepeated here.

Referring to FIG. 7, in operation 710, a fingerprint verificationapparatus receives a query fingerprint image. The query fingerprintimage refers to a fingerprint image, which is target for fingerprintverification, and may be obtained through a fingerprint sensor. Althoughnot illustrated, the fingerprint verification apparatus may performimage preprocessing on the query fingerprint image, as described inoperation 310 of FIG. 3.

In operation 720, the fingerprint verification apparatus determines afirst similarity between the query fingerprint image and each ofregistered fingerprint images. In an example, the fingerprintverification apparatus uses, as the first similarity, a feature valueobtained through an image frequency information-based matching method,such as, Fourier-Mellin transform. Additional detail on thedetermination of the first similarity based on the Fourier-Mellintransform is described with reference to FIGS. 10 through 12. A methodof determining the first similarity between the query fingerprint imageand a registered fingerprint image is not limited to the Fourier-Mellintransform, and various methods may be used to determine a similaritybetween fingerprint patterns without departing from the spirit and scopeof the illustrative examples described.

In operation 730, the fingerprint verification apparatus selects atarget registered fingerprint image group from registered fingerprintimage groups based on the determined first similarity. The fingerprintverification apparatus selects a target registered fingerprint image,which is a registered fingerprint image having a greatest firstsimilarity among the registered fingerprint images, and selects aregistered fingerprint image group including the selected targetregistered fingerprint image to be the target registered fingerprintimage group. As described above, fingerprint images registered in afingerprint registration process may be classified into a plurality ofregistered fingerprint image groups, and the fingerprint verificationapparatus may select a registered fingerprint image group from theregistered fingerprint image groups based on the first similarity toperform fingerprint comparison more precisely and accurately.

In operation 740, the fingerprint verification apparatus determines asecond similarity between the query fingerprint image and each of one ormore registered fingerprint images included in the target registeredfingerprint image group based on matching relationship informationassociated with a matching relationship between the registeredfingerprint images included in the target registered fingerprint imagegroup.

In one example, the fingerprint verification apparatus determines amatching rotation angle between the query fingerprint image and thetarget registered fingerprint image, and arranges the query fingerprintimage and the target registered fingerprint image based on thedetermined matching rotation angle. In another example, the fingerprintverification apparatus arranges the query fingerprint image and thetarget registered fingerprint image using the matching rotation angleobtained in operation 720 of determining the first similarity. Thefingerprint verification apparatus determines a second similaritybetween the query fingerprint image and the target registeredfingerprint image in a matching region between the arranged queryfingerprint image and the arranged target registered fingerprint image.

In an example, the fingerprint verification apparatus determines thesecond similarity between the query fingerprint image and the targetregistered fingerprint image using a method having a greatercomputational complexity than a method used to determine the firstsimilarity. For example, phase correlation information obtained throughthe Fourier-Mellin transform is used to determine the first similarityin operation 720. Whereas, to determine the second similarity, rotationinformation and translation information between fingerprint images maybe used, in addition to the phase correlation information. Thedetermination of second similarity based on the Fourier-Mellin transformwill be described in greater detail with reference to FIGS. 10 through12.

The fingerprint verification apparatus determines a second similaritybetween the query fingerprint image and another registered fingerprintimage included in the target registered fingerprint image group based onthe matching rotation angle and the matching relationship informationbetween the query fingerprint image and the target registeredfingerprint image. The second similarity between the query fingerprintimage and the another registered fingerprint image may also bedetermined using the method used to determine the second similaritybetween the query fingerprint image and the target registeredfingerprint image.

The matching relationship information may include information associatedwith a matching rotation angle between the target registered fingerprintimage and a reference registered fingerprint image included in thetarget registered fingerprint image group, and a matching rotation anglebetween the reference registered fingerprint image and anotherregistered fingerprint image included in the target registeredfingerprint image group. Based on relative matching rotation anglesbetween registered fingerprint images, which are included in suchmatching relationship information, the matching rotation angle betweenthe query fingerprint image and another registered fingerprint imageincluded in the target registered fingerprint image group may bedetermined without an additional matching operation. For example, thematching rotation angle between the query fingerprint image and anotherregistered fingerprint image included in the target registeredfingerprint image group may be determined as represented by Equation 1.θ_(a)=φ−(θ_(c)−θ_(b))  [Equation 1]

In Equation 1, θ_(a) denotes a matching rotation angle between a queryfingerprint image and one registered fingerprint image R included in atarget registered fingerprint image group. φ denotes a matching rotationangle between the query fingerprint image and a target registeredfingerprint image, which refers to an angle by which the queryfingerprint image is rotated relative to the target registeredfingerprint image when the query fingerprint image matches the targetregistered fingerprint image. θ_(b) denotes a matching rotation anglebetween the target registered fingerprint image and a referenceregistered fingerprint image included in the target registeredfingerprint image group, and θ_(c) denotes a matching rotation anglebetween the reference registered fingerprint image and the registeredfingerprint image R. Here, since θ_(b) and θ_(c) are determined based onmatching relationship information, a matching rotation angle between thequery fingerprint image and each of registered fingerprint imagesincluded in the target registered fingerprint image group may bedetermined more rapidly and accurately when a value of φ is determined.

In operation 750, the fingerprint verification apparatus determineswhether fingerprint verification is successful based on the determinedsecond similarity. In one example, the fingerprint verificationapparatus determines whether fingerprint verification of the queryfingerprint image is successful based on the second similarity for eachmatching region between the query fingerprint image and each of the oneor more registered fingerprint images included in the target registeredfingerprint image group. Additional details on the determination ofsuccess or failure of the fingerprint verification will be describedwith reference to FIG. 13.

In another example, the fingerprint verification apparatus selects apreset number of second similarities in descending order starting from agreatest second similarity, among respective second similarities of theregistered fingerprint images included in the target registeredfingerprint image group, and determines a score to be used to determinewhether the fingerprint verification is successful based on the selectedsecond similarities. For example, a mean value or a weighted sum of theselected second similarities may be determined to be the score. However,a method of determining the score is not limited to the exampledescribed in the foregoing, and the fingerprint verification apparatusmay use various methods to determine the score. When the score isgreater than a threshold value, the fingerprint verification apparatusdetermines that the fingerprint verification is successful. When thescore is not greater than the threshold value, the fingerprintverification apparatus determines that the fingerprint verification isunsuccessful. In an example, although the score is greater than thethreshold value, the fingerprint verification apparatus may determinethat the fingerprint verification is unsuccessful when a size of amatching region between the query fingerprint image and each of theregistered fingerprint images included in the target registeredfingerprint image group is less than a threshold size.

When the fingerprint verification is determined to be successful, thefingerprint verification apparatus may cancel a lock state of acomputing apparatus connected to the fingerprint verification apparatus,approve of user log-in, or authorize the use of a payment service or afinancial service. When the fingerprint verification is determined to beunsuccessful, the fingerprint verification apparatus may maintain thelock state of the computing apparatus, or restrict the approval of theuser log-in and the use of the payment service or the financial service.

FIG. 8 is a diagram illustrating another example of a fingerprintverification method. The operations in FIG. 8 may be performed in thesequence and manner as shown, although the order of some operations maybe changed or some of the operations omitted without departing from thespirit and scope of the illustrative examples described. Many of theoperations shown in FIG. 8 may be performed in parallel or concurrently.One or more blocks of FIG. 8, and combinations of the blocks, can beimplemented by special purpose hardware-based computer that perform thespecified functions, or combinations of special purpose hardware andcomputer instructions. In addition to the description of FIG. 8 below,the descriptions of FIGS. 1-7 are also applicable to FIG. 8, and areincorporated herein by reference. Thus, the above description may not berepeated here for brevity.

Referring to FIG. 8, in operation 810, a fingerprint verificationapparatus receives a query fingerprint image a. In operation 820, thefingerprint verification apparatus calculates a first similarity betweenthe query fingerprint image a and each b_(i) (where i=1, . . . , N) of Nfingerprint images registered in a fingerprint registration process.Here, i denotes an index used to identify the registered fingerprintimages. In operation 830, the fingerprint verification apparatus selectsa target registered fingerprint image b_(max), which is a registeredfingerprint image having a greatest first similarity S_(max) among theregistered fingerprint images.

In operation 840, the fingerprint verification apparatus determineswhether a first similarity S_(max) of the target registered fingerprintimage b_(max) is greater than a threshold value. In operation 850, whenthe first similarity S_(max) of the target registered fingerprint imageb_(max) is greater than the threshold value, the fingerprintverification apparatus selects, to be a target registered fingerprintimage group, a registered fingerprint image group including the targetregistered fingerprint image b_(max) from a plurality of registeredfingerprint image groups.

In operation 860, the fingerprint verification apparatus calculates asecond similarity between the query fingerprint image “a” and each ofregistered fingerprint images included in the target registeredfingerprint image group. Thus, only second similarities of theregistered fingerprint images included in the target registeredfingerprint image group is calculated among all the registeredfingerprint images. The fingerprint verification apparatus readilydetermines a matching rotation angle between the query fingerprint imagea and each of the registered fingerprint images included in the targetregistered fingerprint image group using matching relationshipinformation associated with a matching relationship between theregistered fingerprint images included in the target registeredfingerprint image group. The fingerprint verification apparatus matchesthe query fingerprint image a and a registered fingerprint image basedon the determined matching rotation angle, and determines the secondsimilarity in a matching region between the query fingerprint image aand the registered fingerprint image. In an example, the secondsimilarity may be determined using a similarity calculation method witha greater accuracy, compared to a similarity calculation method used todetermine the first similarity.

In operation 870, when the first similarity S_(max) of the targetregistered fingerprint image b_(max) not being greater than thethreshold value, the fingerprint verification apparatus calculates asecond similarity between the query fingerprint image a and each b_(i)(i=1, . . . , N) of all the registered fingerprint images, which are notregistered fingerprint images included in a certain registeredfingerprint image group.

In operation 880, the fingerprint verification apparatus determineswhether fingerprint verification is successful based on the secondsimilarity calculated in operation 860 or 870. For example, thefingerprint verification apparatus determines a score based on secondsimilarities of registered fingerprint images, and determines that thefingerprint verification is successful when the score being greater thana threshold value. When the score not being greater than the thresholdvalue, the fingerprint verification apparatus determines that thefingerprint verification is unsuccessful.

FIG. 9 is a diagram illustrating an example of a fingerprintverification method.

Referring to FIG. 9, fingerprint images registered in a fingerprintregistration process are classified into a first registered fingerprintimage group 925 including registered fingerprint images 910, 915, and920, and a second registered fingerprint image group 940 includingregistered fingerprint images 930 and 935. When a query fingerprintimage 950 is input in a fingerprint verification process, a fingerprintverification apparatus determines a first similarity between the queryfingerprint image 950 and each of the registered fingerprint images 910,915, 920, 930, and 935, regardless of whether the registered fingerprintimages 910, 915, 920, 930, and 935 belong to the first registeredfingerprint image group 925 or the second registered fingerprint imagegroup 940.

In one example, when the first similarity between the query fingerprintimage 950 and the registered fingerprint image 935 is determined to begreatest among the first similarities of the registered fingerprintimages 910, 915, 920, 930, and 935, the fingerprint verificationapparatus selects the registered fingerprint image 935 to be a targetregistered fingerprint image, and selects the second registeredfingerprint image group 940 including the registered fingerprint image935 to be a target registered fingerprint image group. The fingerprintverification apparatus determines a second similarity between the queryfingerprint image 950 and each of the registered fingerprint images 930and 935 included in the second registered fingerprint image group 940based on matching relationship information associated with theregistered fingerprint images 930 and 935. The fingerprint verificationapparatus determines whether fingerprint verification is successfulbased on the determined second similarity.

In another example, when the first similarity of the registeredfingerprint image 935, which is the greatest first similarity, is notgreater than a threshold value, the fingerprint verification apparatusdetermines the second similarity between the query fingerprint image 950and each of the registered fingerprint images 910, 915, 920, 930, and935, and then determines whether the fingerprint verification issuccessful based on the determined second similarity.

FIGS. 10 through 12 are diagrams illustrating examples of a method ofdetermining a similarity between fingerprint images based on aFourier-Mellin transform.

Referring to FIG. 10, in operation 1010, spatial domain informationincluded in a query fingerprint image is transformed to frequency domaininformation through an FFT. In operation 1030, spatial domaininformation included in a registered fingerprint image is transformed tofrequency domain information through an FFT. In an example, thefrequency domain information is based on an orthogonal coordinate systemthat represents information using, for example, two-dimensional (2D) (x,y) coordinates.

In operation 1015, a coordinate system of the frequency domaininformation included in the query fingerprint image is transformed to apolar coordinate system through a log-polar transform (LPT). Forexample, the LPT may be performed on a magnitude value of each pixel inan FFT image obtained through the FFT. In the polar coordinate system,information may be represented by a radius, an angle, or a combinationthereof. In operation 1035, an LPT is also applied to the frequencydomain information included in the registered fingerprint image. Furtherdetails regarding LPT are provided with reference to FIG. 11.

FIG. 11 is a diagram illustrating an example of an LPT. Referring toFIG. 11, concentric circles are set based on a central point 1110 in anorthogonal coordinate system. The concentric circles may be divided intoa plurality of regions based on a radius, an angle, or a combinationthereof. For example, an LPT may map the regions (radius, angle) in theorthogonal coordinate system to regions in a polar coordinate system. Insuch an example, the central point 1110 in the orthogonal coordinatesystem is mapped to a region 1115 corresponding to (0, 0°) in the polarcoordinate system. Similarly, a first region 1120, a second region 1130,a third region 1140, and a fourth region 1150 in the orthogonalcoordinate system are mapped to a first region 1125, a second region1135, a third region 1145, and a fourth region 1155, respectively, inthe polar coordinate system.

Although not illustrated, the LPT may map regions in the orthogonalcoordinate system to regions that are represented based on an angle inthe polar coordinate system. In such a case, the first region 1120 inthe orthogonal coordinate system is mapped to a (0°) region in the polarcoordinate system, the second region 1130 and the third region 1140 inthe orthogonal coordinate system are mapped to a (36°) region in thepolar coordinate system, and the fourth region 1150 in the orthogonalcoordinate system is mapped to a (324°) region in the polar coordinatesystem.

Referring back to FIG. 10, in operation 1020, an FFT is applied to thequery fingerprint image obtained by applying the LPT. In operation 1040,an FFT is applied to the registered fingerprint image obtained byapplying the LPT. In operation 1050, a phase correlation is performedbased on a result of the FFT, and a feature of a peak is detected as aresult of performing the phase correlation. For example, a location ofthe detected peak is represented by rotation information θ for matchingbetween the query fingerprint image and the registered fingerprintimage.

For another example, the location of the detected peak may indicatescale information between the query fingerprint image and a partialfingerprint image. For example, one axis of an image obtained throughthe LPT may correspond to an angle, and the other axis may correspond toa radius. The location of the peak detected by the phase correlation maybe represented by (a coordinate of an axis corresponding to an angle, acoordinate of an axis corresponding to a radius). The coordinate of theaxis corresponding to an angle may indicate matching rotationinformation, and the coordinate of the axis corresponding to a radiusmay indicate scale information.

A fingerprint image does not change in terms of scale, and thus, aradius may be fixed as a preset value, for example, 1. In such a case,the location of the peak detected by the phase correlation may berepresented by the coordinate of the axis corresponding to the angle,and the coordinate of the axis corresponding to the angle may indicaterotation information.

In one example, a peak value may be detected through a phase correlationdescribed herein, and the peak value may be used to determine a firstsimilarity between the query fingerprint image and the registeredfingerprint image. When a size of a region, or a matching region, inwhich fingerprint patterns of the query fingerprint image and theregistered fingerprint image are similar or same, increases, or thefingerprint patterns of the query fingerprint image and the registeredfingerprint image are more similar to each other, the peak value maytend to increase. Based on such a tendency, the first similarity betweenthe query fingerprint image and the registered fingerprint image isdetermined based on the peak value detected through the phasecorrelation.

In operation 1060, the query fingerprint image is rotated based onrotation information θ. In operation 1070, an FFT is applied to therotated query fingerprint image. In operation 1080, a phase correlationis performed based on the query fingerprint image to which the FFT isapplied in operation 1070 and the registered fingerprint image to whichthe FFT is applied in operation 1030. A feature of a peak is detected asa result of the phase correlation, and a location of the detected peakindicates translation information (Tx, Ty) between the query fingerprintimage and the registered fingerprint image. In operation 1090, the queryfingerprint image rotated in operation 1060 is translated based on thetranslation information (Tx, Ty).

The query fingerprint image and the registered fingerprint image may bematched by rotating and translating the query fingerprint image based onrotation information and translation information that are obtainedthrough the Fourier-Mellin transform described above. In an example, thesecond similarity is determined based on a matching region between therotated and translated query fingerprint image and the registeredfingerprint image. In one example, the second similarity is determinedusing a normalized cross correlation (NCC) method based on an imagebrightness value. For example, the second similarity is determined basedon a correlation obtained through Equation 2.

$\begin{matrix}{{{ncc}\left( {I_{1},I_{2}} \right)} = \frac{\sum\limits_{{({i,j})} \in W}{{I_{1}\left( {i,j} \right)} \cdot {I_{2}\left( {{x + i},{y + j}} \right)}}}{\sqrt[2]{\sum\limits_{{({i,j})} \in W}{{I_{1}^{2}\left( {i,j} \right)} \cdot {\sum\limits_{{({i,j})} \in W}{I_{2}^{2}\left( {{x + i},{y + j}} \right)}}}}}} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$

In Equation 2, W denotes a matching region between a rotated andtranslated query fingerprint image I₁ and a registered fingerprint imageI₂, and ncc(I₁, I₂) denotes a correlation in the matching region Wbetween the query fingerprint image I₁ and the registered fingerprintimage I₂. i denotes an x-axis coordinate of a pixel in the matchingregion W, and j denotes a y-axis coordinate of the pixel in the matchingregion W. x denotes translation information (Tx) in an x-axis direction,and y denotes translation information (Ty) in a y-axis direction. I₁(i,j) denotes a pixel value on (i, j) coordinates of the query fingerprintimage I₁, and I₂(x+i, y+j) denotes a pixel value on (x+i, y+j)coordinates of the registered fingerprint image I₂. The correlation inthe matching region W, which is calculated through Equation 2, may beused as the second similarity between the query fingerprint image andthe registered fingerprint image.

Although the example of the rotating and translating of the queryfingerprint image is described with reference to FIG. 10, in an example,the registered fingerprint image may be rotated and translated, in lieuof the query fingerprint image, based on a result of a phasecorrelation. In an example, both the query fingerprint image and theregistered fingerprint image may be rotated and translated for matching.

After the target fingerprint image group is determined in thefingerprint verification process, operations 1010 through 1050 ofre-calculating the rotation information θ may not be needed. Instead,the rotation information θ for matching between the query fingerprintimage and the registered fingerprint image may be determined based onmatching relationship information associated with the registeredfingerprint images. The rotation information θ may correspond to thematching rotation angle θ_(a) in Equation 1 above.

Also, in the fingerprint registration process, a similarity between acurrent fingerprint image input for fingerprint registration and aregistered fingerprint image may be determined using a method ofdetermining the first similarity or the second similarity describedabove. Here, the query fingerprint image and the registered fingerprintimage may be replaced with the current fingerprint image and thepreviously registered fingerprint image, respectively.

FIG. 12 is a diagram illustrating an example of a method of determininga similarity between a query fingerprint image and a registeredfingerprint image based on a Fourier-Mellin transform.

Referring to FIG. 12, a registered fingerprint image 1210 is transformedto a first LPT image 1220 through an FFT and an LPT, and a queryfingerprint image 1215 is transformed to a second LPT image 1225 throughan FFT and an LPT. Through a phase correlation 1230 between the firstLPT image 1220 and the second LPT image 1225, rotation information θbetween the registered fingerprint image 1210 and query fingerprintimage 1215 is determined. Based on a peak value detected through thephase correlation 1230, a first similarity between the registeredfingerprint image 1210 and the query fingerprint image 1215 isdetermined.

The query fingerprint image 1215 is rotated based on the rotationinformation θ determined by the phase correlation 1230. In addition,translation information (Tx, Ty) between the registered fingerprintimage 1210 and the query fingerprint image 1215 is determined by a phasecorrelation 1250 between an FFT image obtained by performing an FFT onthe registered fingerprint image 1210 and an FFT image obtained byperforming an FFT on a rotated query fingerprint image 1240 obtained byrotating the query fingerprint image 1215 based on the rotationinformation θ.

Based on the rotation information θ and the translation information (Tx,Ty), the registered fingerprint image 1210 and the query fingerprintimage 1215 are matched, and a matching region 1270 between theregistered fingerprint image 1210 and the query fingerprint image 1215is determined. In one example, a correlation is calculated with respectto the matching region 1270 between the registered fingerprint image1210 and the query fingerprint image 1215 in a matched image 1260 basedon Equation 2 above, and the calculated correlation is determined to bea second similarity between the registered fingerprint image 1210 andthe query fingerprint image 1215.

FIG. 13 is a diagram illustrating an example of a method of determiningwhether fingerprint verification is successful based on a matchingregion-based similarity.

Referring to FIG. 13, when a target registered fingerprint image groupis selected, a fingerprint verification apparatus determines whetherfingerprint verification is successful based on a second similaritybetween a query fingerprint image 1310 and each of registeredfingerprint images 1322, 1324, 1326, and 1328 included in the targetregistered fingerprint image group. In an example, the fingerprintverification apparatus performs the fingerprint verification basedfurther on a matching region between the query fingerprint image 1310and each of the registered fingerprint images 1322, 1324, 1326, and1328, in addition to the second similarity between the query fingerprintimage 1310 and each of the registered fingerprint images 1322, 1324,1326, and 1328. The fingerprint verification apparatus determines amatching region-based similarity corresponding to the query fingerprintimage 1310. The matching region-based similarity is represented by asecond similarity between the query fingerprint image 1310 and each ofthe registered fingerprint images 1322, 1324, 1326, and 1328, allocatedto a corresponding region of the query fingerprint image 1310. In oneexample, the fingerprint verification apparatus determines a matchingregion and a second similarity each time the fingerprint verificationapparatus compares the query fingerprint image 1310 and each of theregistered fingerprint images 1322, 1324, 1326, and 1328, and updatesthe matching region-based similarity based on the determined matchingregion and the second similarity corresponding to the matching region.

The matching region-based similarity is visually represented by areference numeral 1330. In an example, the matching region-basedsimilarity includes information associated with the matching regionbetween the query fingerprint image 1310 and each of the registeredfingerprint images 1322, 1324, 1326, and 1328, and corresponding secondsimilarities. The determination of the matching region-based similarityis described in detail as follows.

As illustrated, the fingerprint verification apparatus determines amatching region 1332 between the query fingerprint image 1310 and theregistered fingerprint image 1322, and a corresponding second similarityS₁. The fingerprint verification apparatus updates the matchingregion-based similarity by allocating the second similarity S₁ to thematching region 1332 of an entire region corresponding to the queryfingerprint image 1310. In stage 1340, the fingerprint verificationapparatus determines whether a score determined based on the matchingregion-based similarity satisfies a fingerprint verification condition.In an example, the score is a reference value used to determine whetherfingerprint verification is successful.

When the score does not satisfy the fingerprint verification condition,the fingerprint verification apparatus compares the query fingerprintimage 1310 and the registered fingerprint image 1324. The fingerprintverification apparatus determines a matching region 1334 between thequery fingerprint image 1310 and the registered fingerprint image 1324,and a corresponding second similarity S₂. The fingerprint verificationapparatus then updates the previously determined matching region-basedsimilarity based on the matching region 1334 and the second similarityS₂. In stage 1340, the fingerprint verification apparatus determineswhether a score determined based on the updated matching region-basedsimilarity satisfies the fingerprint verification condition. When thescore does not satisfy the fingerprint verification condition, thefingerprint verification apparatus performs the operations described inthe foregoing on the registered fingerprint image 1326. The matchingregion-based similarity is then updated again based on a matching region1336 between the query fingerprint image 1310 and the registeredfingerprint image 1326 and a corresponding second similarity S₃.

As described above, the fingerprint verification apparatus maycontinuously update the matching region-based similarity by comparingthe query fingerprint image 1310 and registered fingerprint images, forexample, the registered fingerprint images 1322, 1324, 1326, and 1328,included in the target registered fingerprint image group in sequentialorder, until the fingerprint verification condition is satisfied.Whether the fingerprint verification condition is satisfied or not isdetermined each time the matching region-based similarity is updated,and thus rapid processing may be enabled because a fingerprintverification process is terminated after only some registeredfingerprint images, instead of all the registered fingerprint images, iscompared to the query fingerprint image 1310.

Although the example relates to determining whether the fingerprintverification condition is satisfied or not each time the matchingregion-based similarity is updated, in an example, the determining ofwhether the fingerprint verification condition is satisfied may beperformed last. For example, all respective second similarities betweenthe query fingerprint image 1310 and the registered fingerprint images1322, 1324, 1326, and 1328 may be determined and a matching region-basedsimilarity may be determined based on corresponding matching regions andthe second similarities, and then whether the fingerprint verificationcondition is satisfied may be determined based on the determinedmatching region-based similarity.

The fingerprint verification apparatus may determine whether thefingerprint verification is successful based on various fingerprintverification conditions, and a scope of examples is not limited to theexample described in the foregoing.

FIG. 14 is a diagram illustrating an example of a fingerprintverification apparatus 1420, which may be configured to perform any oneor combination or all operations described above.

Referring to FIG. 14, in an example, a fingerprint sensor 1410 obtainsfingerprint information of a user attempting fingerprint verification,and generates a query fingerprint image based on the obtainedfingerprint information. The generated query fingerprint image may betransferred to the fingerprint verification apparatus 1420, and thefingerprint verification apparatus 1420 may perform the fingerprintverification by comparing the query fingerprint image and at least oneregistered fingerprint image stored in a registered fingerprint image DB1430. The user may register in advance at least one fingerprint image ina fingerprint registration process, and the registered fingerprint imagemay be stored in the registered fingerprint image DB 1430. According toan example, the fingerprint sensor 1410 may be included in thefingerprint verification apparatus 1420.

The fingerprint verification apparatus 1420 may perform one or more ofthe fingerprint verification operation described herein, and provide theuser with a result of the fingerprint verification. The fingerprintverification apparatus 1420 may output the result of the fingerprintverification in a form of, for example, a voice, a vibration, a letter,an image, or a video. However, a scope of examples is not limited to theexamples described in the foregoing, and the fingerprint verificationapparatus 1420 may output the result of the fingerprint verification invarious forms.

The fingerprint verification apparatus 1420 includes at least oneprocessor 1422 and a memory 1424. The memory 1424 is a non-transitorycomputer readable medium or device connected to the processor 1422, andstores instructions, which when executed by the processor 1422, causingthe processor 1422 to implement one or more or all operations describedherein. The memory 1424 may further store data to be processed by theprocessor 1422 or data having been processed by the processor 1422. Thememory 1424 includes, for example, a high-speed random access memory(RAM) and/or a nonvolatile computer-readable storage medium (e.g., atleast one disk storage device, a flash memory device, or othernonvolatile solid state memory devices). Further description of thememory 1424 is provided below.

The processor 1422 may be configured to perform one or more or alloperations described with reference to FIGS. 1, 2A, 2B, and 7 through13. The processor 1422 may implement instructions to perform one or moreor all operations in a fingerprint verification process. In one example,the processor 1422 may be configured to receive a query fingerprintimage, and determine a first similarity between the query fingerprintimage and each of registered fingerprint images. The processor 1422 mayselect a target registered fingerprint image group from registeredfingerprint image groups based on the determined first similarity. Forexample, the processor 1422 may select, to be a target registeredfingerprint image, a registered fingerprint image having a greatestfirst similarity among all the registered fingerprint images, and selecta registered fingerprint image group including the selected targetregistered fingerprint image to be the target registered fingerprintimage group. The processor 1422 may determine a second similaritybetween the query fingerprint image and each of registered fingerprintimages included in the target registered fingerprint image group basedon matching relationship information associated with a matchingrelationship between the registered fingerprint images included in thetarget registered fingerprint image group. For example, the processor1422 may determine a second similarity between the query fingerprintimage and the target registered fingerprint image, and then determine asecond similarity between the query fingerprint image and anotherregistered fingerprint image included in the target registeredfingerprint image group based on a matching rotation angle between thequery fingerprint image and the target registered fingerprint image andthe matching relationship information. The processor 1422 may determinewhether fingerprint verification of the query fingerprint image issuccessful based on the determined second similarity.

FIG. 15 is a diagram illustrating an example of a computing apparatus.

Referring to FIG. 15, a computing apparatus 1500 performs a fingerprintregistration process and a fingerprint verification process. In thefingerprint registration process, the computing apparatus 1500 obtainsfingerprint images of a user who desires to register fingerprints of theuser, and generates a plurality of registered fingerprint image groupsinto which the obtained fingerprint images are classified. In anexample, the computing apparatus 1500 determines matching relationshipinformation associated with a matching relationship between registeredfingerprint images included in each of the registered fingerprint imagegroups, and stores the determined matching relationship informationalong with the registered fingerprint images. In the fingerprintverification process, the computing apparatus 1500 obtains a fingerprintimage of a user who attempts at fingerprint verification, and performthe fingerprint verification by comparing the obtained fingerprint imageand the registered fingerprint images based on the matching relationshipinformation determined in the fingerprint registration process.

The computing apparatus 1500 includes a processor 1510, a memory 1520, afingerprint sensor 1530, a storage device 1540, an input device 1550, anoutput device 1560, and a network interface 1570. The processor 1510,the memory 1520, the fingerprint sensor 1530, the storage device 1540,the input device 1550, the output device 1560, and the network interface1570 may communicate with one another through a communication bus 1580.

In response to a fingerprint input from a user, the fingerprint sensor1530 may obtain a fingerprint image for fingerprint registration orfingerprint verification. For example, when a finger of the user touchesa sensing region or a finger of the user swipes at the sensing region,the fingerprint sensor 1530 may sense a fingerprint of the finger. Inanother example, in a case in which the fingerprint sensor 1530 isintegrated in a display, the sensing region may be represented by asurface of the display, and the fingerprint sensor 1530 may sense afingerprint from a finger that is in contact with the display.

The processor 1510 may implement functions and instructions to operatein the computing apparatus 1500 as described herein. For example, theprocessor 1510 executes instructions stored in the memory 1520 or thestorage device 1540. The processor 1510 may be configured to perform oneor more, any combination, or all operations described with reference toFIGS. 1 through 14. For example, the processor 1510 may be configured toperform a fingerprint registration process and a fingerprintverification process. In the fingerprint registration process, theprocessor 1510 may perform fingerprint registration on a currentfingerprint image obtained through the fingerprint sensor 1530. Toperform the fingerprint registration, the processor 1510 may determine asimilarity between the current fingerprint image and a previouslyregistered fingerprint image, and classify registered fingerprint imagesinto registered fingerprint image groups based on the determinedsimilarity. For example, in response to the similarity between thecurrent fingerprint image and the registered fingerprint imagesatisfying a preset condition, the processor 1510 may classify thecurrent fingerprint image and the registered fingerprint image into asame registered fingerprint image group, and determine matchingrelationship information associated with a matching relationship betweenthe current fingerprint image and a reference registered fingerprintimage included in the registered fingerprint image group. The matchingrelationship information may include, for example, a matching rotationangle between the current fingerprint image and the reference registeredfingerprint image, and a group identifier of the registered fingerprintimage group that is assigned to the current fingerprint image. Theprocessor 1510 may then store the determined matching relationshipinformation in the storage device 1540. In the fingerprint verificationprocess, the processor 1510 may perform one or more, any combination, orall operations that are performed by the fingerprint verificationapparatus 1420 described with reference to FIG. 14, and thus a moredetailed and repeated description will be omitted for brevity.

The storage device 1540 includes a computer-readable storage medium or acomputer-readable storage device. The storage device 1540 may store a DBincluding registered fingerprint images and matching relationshipinformation between the registered fingerprint images. The storagedevice 1540 includes, for example, a magnetic hard disk, an opticaldisc, a flash memory, an erasable programmable read-only memory (EPROM),a floppy disk, or other types of nonvolatile memories. Furtherdescription of the storage device 1540 is provided below.

The input device 1550 may receive an input from a user through atactile, video, audio, or touch input. The input device 1550 includes,for example, a keyboard, a mouse, a touchscreen, a display, amicrophone, a fingerprint reader, a retinal scanner, and other devicesconfigured to detect the input from the user and transmit the detectedinput to the computing apparatus 1500.

The output device 1560 may provide a user with an output of thecomputing apparatus 1500 through a visual, auditory, or tactile channel.For example, the output device 1560 may visualize information related tothe fingerprint verification and provide the user with the visualizedinformation. For example, the visualized information may indicatewhether the fingerprint verification is successful, or may enable accessto further functions of the computing apparatus 1500 demonstratedthrough the visualized information. The output device 1560 includes, forexample, a liquid crystal display (LCD), a light-emitting diode (LED)display, a touchscreen, a speaker, a vibration generator, and otherdevices configured to provide the output to the user.

The network interface 1570 may communicate with an external devicethrough a wired or wireless network. The network interface 1570includes, for example, an Ethernet card, an optical transceiver, a radiofrequency transceiver, and other network interface cards configured totransmit and receive information. The network interface 1570 maywirelessly communicate with the external device using a communicationmethod, such as, for example, Bluetooth, WiFi, or a third generation(3G), fourth generation (4G), or fifth generation (5G) communicationmethod.

The computing apparatus 100, fingerprint verification apparatus 1420,computing apparatus 1500, and other apparatuses, units, modules,devices, and components illustrated in FIGS. 1, 14 and 15 that performthe operations described in this application are implemented by hardwarecomponents configured to perform the operations described in thisapplication that are performed by the hardware components. Examples ofhardware components that may be used to perform the operations describedin this application where appropriate include controllers, sensors,generators, drivers, memories, comparators, arithmetic logic units,adders, subtractors, multipliers, dividers, integrators, and any otherelectronic components configured to perform the operations described inthis application. In other examples, one or more of the hardwarecomponents that perform the operations described in this application areimplemented by computing hardware, for example, by one or moreprocessors or computers. A processor or computer may be implemented byone or more processing elements, such as an array of logic gates, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a programmable logic controller, a field-programmablegate array, a programmable logic array, a microprocessor, or any otherdevice or combination of devices that is configured to respond to andexecute instructions in a defined manner to achieve a desired result. Inone example, a processor or computer includes, or is connected to, oneor more memories storing instructions or software that are executed bythe processor or computer. Hardware components implemented by aprocessor or computer may execute instructions or software, such as anoperating system (OS) and one or more software applications that run onthe OS, to perform the operations described in this application. Thehardware components may also access, manipulate, process, create, andstore data in response to execution of the instructions or software. Forsimplicity, the singular term “processor” or “computer” may be used inthe description of the examples described in this application, but inother examples multiple processors or computers may be used, or aprocessor or computer may include multiple processing elements, ormultiple types of processing elements, or both. For example, a singlehardware component or two or more hardware components may be implementedby a single processor, or two or more processors, or a processor and acontroller. One or more hardware components may be implemented by one ormore processors, or a processor and a controller, and one or more otherhardware components may be implemented by one or more other processors,or another processor and another controller. One or more processors, ora processor and a controller, may implement a single hardware component,or two or more hardware components. A hardware component may have anyone or more of different processing configurations, examples of whichinclude a single processor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 3, 4, 7, 8, and 10 that perform theoperations described in this application are performed by computinghardware, for example, by one or more processors or computers,implemented as described above executing instructions or software toperform the operations described in this application that are performedby the methods. For example, a single operation or two or moreoperations may be performed by a single processor, or two or moreprocessors, or a processor and a controller. One or more operations maybe performed by one or more processors, or a processor and a controller,and one or more other operations may be performed by one or more otherprocessors, or another processor and another controller. One or moreprocessors, or a processor and a controller, may perform a singleoperation, or two or more operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software includes at least one of an applet, a dynamiclink library (DLL), middleware, firmware, a device driver, anapplication program storing the method of preventing the collision. Inone example, the instructions or software include machine code that isdirectly executed by the one or more processors or computers, such asmachine code produced by a compiler. In another example, theinstructions or software includes higher-level code that is executed bythe one or more processors or computer using an interpreter. Theinstructions or software may be written using any programming languagebased on the block diagrams and the flow charts illustrated in thedrawings and the corresponding descriptions in the specification, whichdisclose algorithms for performing the operations that are performed bythe hardware components and the methods as described above.

The instructions or software to control a processor or computer toimplement the hardware components and perform the methods as describedabove, and any associated data, data files, and data structures, arerecorded, stored, or fixed in or on one or more non-transitorycomputer-readable storage media. Examples of a non-transitorycomputer-readable storage medium include read-only memory (ROM),random-access programmable read only memory (PROM), electricallyerasable programmable read-only memory (EEPROM), random-access memory(RAM), dynamic random access memory (DRAM), static random access memory(SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs,CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs,BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-ray or optical disk storage,hard disk drive (HDD), solid state drive (SSD), flash memory, a cardtype memory such as multimedia card micro or a card (for example, securedigital (SD) or extreme digital (XD)), magnetic tapes, floppy disks,magneto-optical data storage devices, optical data storage devices, harddisks, solid-state disks, and any other device that is configured tostore the instructions or software and any associated data, data files,and data structures in a non-transitory manner and providing theinstructions or software and any associated data, data files, and datastructures to a processor or computer so that the processor or computercan execute the instructions. In one example, the instructions orsoftware and any associated data, data files, and data structures aredistributed over network-coupled computer systems so that theinstructions and software and any associated data, data files, and datastructures are stored, accessed, and executed in a distributed fashionby the one or more processors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. A fingerprint verification method comprising:receiving a query fingerprint image; determining a first similaritybetween the query fingerprint image and each of registered fingerprintimages; selecting a target registered fingerprint image group fromregistered fingerprint image groups based on the first similarity;determining a second similarity between the query fingerprint image andeach of registered fingerprint images in the target registeredfingerprint image group based on matching relationship informationbetween the registered fingerprint images in the target registeredfingerprint image group; and determining whether fingerprintverification of the query fingerprint image is successful based on thesecond similarity.
 2. The fingerprint verification method of claim 1,wherein the matching relationship information comprises informationassociated with a matching rotation angle between the registeredfingerprint images in the target registered fingerprint image group, andthe matching rotation angle is determined in a fingerprint registrationprocess.
 3. The fingerprint verification method of claim 1, wherein theselecting of the target registered fingerprint image group comprises:selecting a target registered fingerprint image, which is a registeredfingerprint image having a greatest first similarity among theregistered fingerprint images; and selecting, to be the targetregistered fingerprint image group, a registered fingerprint image groupincluding the target registered fingerprint image.
 4. The fingerprintverification method of claim 3, wherein the determining of the secondsimilarity comprises: determining a second similarity between the queryfingerprint image and the target registered fingerprint image; anddetermining a second similarity between the query fingerprint image andanother registered fingerprint image included in the target registeredfingerprint image group based on a matching rotation angle between thequery fingerprint image and the target registered fingerprint image, andthe matching relationship information.
 5. The fingerprint verificationmethod of claim 4, wherein the matching relationship informationcomprises a matching rotation angle between the target registeredfingerprint image and a reference registered fingerprint image includedin the target registered fingerprint image group, and a matchingrotation angle between the reference registered fingerprint image andthe other registered fingerprint image.
 6. The fingerprint verificationmethod of claim 3, wherein the determining of the second similaritycomprises: determining a matching rotation angle between the queryfingerprint image and the target registered fingerprint image; arrangingthe query fingerprint image and the target registered fingerprint imagebased on the determined matching rotation angle; and determining asecond similarity between the query fingerprint image and the targetregistered fingerprint image in a matching region in which the arrangedquery fingerprint image and the arranged target registered fingerprintimage match each other.
 7. The fingerprint verification method of claim1, wherein the second similarity is determined with a greatercomputational complexity than used to determine the first similarity. 8.The fingerprint verification method of claim 1, wherein the determiningof whether the fingerprint verification is successful comprises:determining whether the fingerprint verification is successful based ona second similarity of a matching region between the query fingerprintimage and at least one registered fingerprint image included in thetarget registered fingerprint image group.
 9. A non-transitorycomputer-readable storage medium storing instructions, that whenexecuted by processor, cause the processor to implement the method ofclaim
 1. 10. A fingerprint verification apparatus comprising: aprocessor configured to: receive a query fingerprint image; determine afirst similarity between the query fingerprint image and each ofregistered fingerprint images; select a target registered fingerprintimage group from registered fingerprint image groups based on the firstsimilarity; determine a second similarity between the query fingerprintimage and each of registered fingerprint images in the target registeredfingerprint image group based on matching relationship informationbetween the registered fingerprint images in the target registeredfingerprint image group; and determine whether fingerprint verificationof the query fingerprint image is successful based on the secondsimilarity.
 11. The fingerprint verification apparatus of claim 10,wherein the processor is further configured to select a targetregistered fingerprint image, which is a registered fingerprint imagehaving a greatest first similarity among the registered fingerprintimages, and to select a registered fingerprint image group including thetarget registered fingerprint image to be the target registeredfingerprint image group.
 12. The fingerprint verification apparatus ofclaim 11, wherein the processor is further configured to determine asecond similarity between the query fingerprint image and the targetregistered fingerprint image, and to determine a second similaritybetween the query fingerprint image and another registered fingerprintimage included in the target registered fingerprint image group based ona matching rotation angle between the query fingerprint image and thetarget registered fingerprint image and the matching relationshipinformation.
 13. The fingerprint verification apparatus of claim 10,wherein the matching relationship information comprises informationassociated with a matching rotation angle between the registeredfingerprint images in the target registered fingerprint image group, andthe matching rotation angle is determined in a fingerprint registrationprocess.